Context-Aware Machine Learning for Intelligent Transportation Systems: A Survey

Huang, Guang-Li; Zaslavsky, Arkady; Loke, Seng W.; Abkenar, Amin B.; Medvedev, Alexey; Hassani, Alireza

doi:10.1109/tits.2022.3216462

Cited by 11 publications

(9 citation statements)

References 115 publications

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“…Classifying Internet traffic according to the application that generates it is an important task for network planning and designing, QoS, and traffic management [21] [22] [23]. In particular, detecting traffic with a delay-tolerant nature is a potentially important problem for this project that targets offloading it to transportation and social networks.…”

Section: E Context-aware Traffic Classification Frameworkmentioning

confidence: 99%

A Sustainable Data Dissemination Approach by Utilizing the Internet of Moving Things

Memon,

Ali,

Kim

2024

IEEE Access

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The Internet of Things (IoT) has shown remarkable competence in uniting devices and internet connectivity to make modern life's applications a reality. These networked devices, empowered with computing and communications, usually exhibit an exponential increment in network traffic to enhance the end-user quality of experience (QoE). The exponential increase in network traffic makes the traditional communication system congested. To reduce the burden of network traffic, the data could be offloaded to device-to-device(D2D) based infrastructure. This offloading scheme results in an innovative, eco-friendly, and sustainable data dissemination approach known as social networking and is mostly done in opportunistic ways. In this paper, we propose a scheme to explore the performance of the existing routing protocols with the movements and spatial closeness relations among 'things', termed as Internet of Moving Things (IoMT). The spatial closeness known as traces will help to make optimal data offloading decisions while considering the mobility traces of humans in the social network. The proposed mechanism is designed to optimally utilize moving physical objects to be involved as a third party in the data communication process between the sources and the destinations rather than using the traditional infrastructure while considering the quality of service (QoS). The simulation results have corroborated the proposed scheme and showed the remarkable performance of routing algorithms while incorporating mobility traces.

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Section: E Context-aware Traffic Classification Frameworkmentioning

confidence: 99%

A Sustainable Data Dissemination Approach by Utilizing the Internet of Moving Things

Memon,

Ali,

Kim

2024

IEEE Access

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“…ITS utilizing vehicle-to-everything (V2X) technology [6] meet the demands of modern transport systems [7][8][9] by continuously collecting data on road traffic through various sensors [4]. The collected and systematized data, which represent the context of traffic situations, are crucial for determining the evolving requirements of ITS functionality over time [10]. Context data play a vital role in analyzing sensor data, reasoning, and modeling intelligent systems, significantly impacting the effectiveness of ITSs.…”

Section: Implementation Of Intelligent Transport Systems Via Simulati...mentioning

confidence: 99%

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Lopukhova,

Abdulnagimov,

Voronkov

et al. 2024

Applied Sciences

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Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. The proposed method for creating and calibrating a traffic simulation model has significantly improved the statistical correspondence between the generated vehicle characteristics and real data about cars on the simulated road section. The correspondence has increased from 37% to 73%. Machine learning models trained on generated data and tested on real data show improved accuracy rates. Mean absolute error, mean square error, and mean absolute percentage error decreased by more than two orders of magnitude. The coefficient of determination has also increased, approaching 1. This method eliminates the need to deploy wireless sensor networks, which can reduce the cost of implementing intelligent transport systems.

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“…It is worth noting that environmental perception in autonomous driving systems heavily relies on deep learning technology 3 . Prior to the application of deep learning in machine vision, visual perception technology was largely stagnant 4 , 5 . Images captured by onboard cameras are critical for intelligent perception in autonomous driving systems.…”

Section: Introductionmentioning

confidence: 99%

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

Qiu,

Tang,

Yang

et al. 2024

Sci Rep

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The resolution of traffic congestion and personal safety issues holds paramount importance for human’s life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving. Aiming at the problem of small target detection in traditional YOLOv5s, this paper proposes an optimized target detection algorithm. The C3 module on the algorithm’s backbone is upgraded to the CBAMC3 module, introducing a novel GELU activation function and EfficiCIoU loss function, which accelerate convergence on position loss lbox, confidence loss lobj, and classification loss lcls, enhance image learning capabilities and address the issue of inaccurate detection of small targets by improving the algorithm. Testing with a vehicle-mounted camera on a predefined route effectively identifies road vehicles and analyzes depth position information. The avoidance model, combined with Pure Pursuit and MPC control algorithms, exhibits more stable variations in vehicle speed, front-wheel steering angle, lateral acceleration, etc., compared to the non-optimized version. The robustness of the driving system's visual avoidance functionality is enhanced, further ameliorating congestion issues and ensuring personal safety.

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Context-Aware Machine Learning for Intelligent Transportation Systems: A Survey

Cited by 11 publications

References 115 publications

A Sustainable Data Dissemination Approach by Utilizing the Internet of Moving Things

A Sustainable Data Dissemination Approach by Utilizing the Internet of Moving Things

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

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